Coding the Uncoded: Embedding Social Determinants into Health Information Systems
Modern medicine has mastered the art of tracking vitals, imaging organs, and even sequencing the genome. But the more significant influencers of health, such as poverty, education, housing, transportation, and food insecurity, have long remained hidden from structured health records. These are not clinical anomalies. They are the Social Determinants of Health (SDOH), and they often dictate the trajectory of wellness more than any prescription.
To understand the whole patient, our systems must go beyond the clinic walls. Integrating SDOH into Health Information Systems (HIS) represents one of the most crucial evolutions in digital health today. It's about enabling technology to see what the stethoscope cannot.
Why SDOH Matters More Than We Think?
According to the World Health Organization (WHO), up to 55% of health outcomes are influenced by social and environmental factors, with only 10–15% directly influenced by clinical care (WHO, 2023). Whether it’s access to clean water, stable housing, a nutritious diet, or job security, these non-clinical variables have a massive impact on chronic disease prevalence, hospital readmissions, and health behaviors.
For instance, a patient discharged with a new insulin regimen but no refrigerator at home is set up to fail, no matter how evidence-based the prescription. Unless health systems acknowledge these upstream factors, care remains incomplete, and often inequitable.
Yet, most electronic health records (EHRs) and hospital information systems barely scratch the surface of capturing or using this kind of data.
The Current State: Patchy, Passive, and Peripheral
While some health systems have begun experimenting with social needs screening tools, SDOH data collection remains optional, inconsistent, and largely unstandardized. One hospital might use custom intake forms, another might rely on voluntary checklists, and others may not collect any SDOH data at all.
Furthermore, most HIS platforms are not designed to effectively analyze, act on, or share SDOH data. Often, this information is relegated to free-text fields in progress notes, making it inaccessible to algorithms, care teams, or population health platforms.
This lack of structured integration has a direct impact on:
- Care coordination
- Predictive analytics
- Community resource referrals
- Value-based payment models
In essence, SDOH is trapped in narrative rather than being harnessed as a strategic data layer.
A Blueprint for Integration: Designing SDOH-Aware Information Systems
So, how do we reimagine health information systems to include these “non-medical” factors in a clinically meaningful way?
Standardizing SDOH Terminologies and Codes
The adoption of LOINC, SNOMED CT, and ICD-10 Z-codes has enabled some structuring of social needs, but uptake remains low. For example, ICD-10’s Z55–Z65 codes address problems related to education, employment, housing, and economic circumstances. However, a 2021 study showed that less than 2% of EHRs coded any Z-code data, even when social risk was evident (Cantor et al., 2021).
To change this, systems must make SDOH documentation a standard part of intake workflows, with dropdown menus, templates, and integration into clinical decision support tools.
Embedding SDOH in Clinical Workflows and Alerts
It’s not enough to record SDOH but clinicians need to see and act on it. Embedding risk factors, such as housing instability or food insecurity, into dashboards, flags, or triage scores helps frontline providers prioritize resources and referrals. Services and tools like Unite Us and Aunt Bertha can be integrated directly into EHRs to connect patients with local social support agencies based on coded needs.
Aligning with Interoperability Standards
FHIR’s SDOH Implementation Guide, developed by HL7’s Gravity Project, offers a roadmap for standardizing SDOH data exchange across systems and providers. This supports the bidirectional flow of social risk data from hospitals to community-based organizations and vice versa.
With this, we reach closer to systems that understand not just how a patient is treated, but how they live.
Case Study: SDOH Integration at New York’s Mount Sinai Health System
Mount Sinai’s EHR now includes a structured social needs intake module aligned with the PRAPARE (Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences) tool. Data gathered, ranging from housing conditions to immigration status, is automatically flagged in patient dashboards.
They’ve also integrated community referral networks that close the loop, ensuring that if a patient is referred to a shelter or food bank, the outcome is tracked. Early results have shown reductions in missed appointments and improved adherence to care plans.
It’s a reminder that when care teams understand the context of a patient’s life, clinical outcomes improve.
Ethical and Governance Considerations
Integrating SDOH data also raises important questions about data privacy, consent, and patient trust. Is a patient comfortable disclosing domestic violence or food insecurity if they fear judgment or repercussions?
Health systems must ensure that data sensitivity is considered with robust data protection, and that SDOH data is not misused for risk scoring or to perpetuate insurance bias. Ethical frameworks, community engagement, and transparent communication are vital.
From Transactional to Transformational Health Records
The true power of health information systems lies not just in their ability to record encounters but in their potential to understand and transform lives. SDOH data is not noise but it’s narrative. It tells the story behind the numbers.
A well-designed HIS that captures this story can shift the system from a reactive to a proactive approach, from an episodic to a holistic perspective, and from treating illness to promoting wellness.
References
- World Health Organization. Social Determinants of Health. 2023. https://www.who.int/health-topics/social-determinants-of-health
- Cantor MN, Thorpe L. Integrating Data on Social Determinants of Health Into Electronic Health Records. Health Affairs. 2021;40(2):265–270. https://doi.org/10.1377/hlthaff.2020.01502
- HL7 Gravity Project. SDOH Clinical Care FHIR Implementation Guide. HL7; 2022. https://www.hl7.org/gravity
- Centers for Medicare & Medicaid Services (CMS). Z Codes Utilization among Medicare Fee-for-Service (FFS) Beneficiaries in 2019. 2021. https://www.cms.gov
- De Marchis EH, Hessler D, Fichtenberg C, et al. Assessment of SDOH Documentation in US Health Systems. JAMA Netw Open. 2021;4(4):e2111380. https://doi.org/10.1001/jamanetworkopen.2021.11380
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